Araştırma Makalesi
BibTex RIS Kaynak Göster

Yazılım Geliştirme Öğreniminde Beceri Derinliği ve Dil Yeterliliğinin Yapay Zekâ ile Entegrasyonu

Yıl 2024, , 382 - 399, 20.12.2024
https://doi.org/10.32329/uad.1519587

Öz

Üretici yapa zekâ modelleri günümüzde birçok alanda oldukça etkilidir. Yazılım geliştirme ve programlama alanında büyük bir etkiye sahip olduğu da son zamanlarda sıkça tartışılmaktadır. Bu çalışmada lisans ve ön lisans düzeyinde yazılım, bilgisayar ve programlama eğitimi alan bireyle gelişen ve hızla güçlenen bu teknoloji karşısındaki düşüncelerini öğrenmek amacıyla anket uygulanmıştır. Araştırmanın çalışma grubu, 2023-2024 eğitim-öğretim yılı bahar yarıyılında Trakya Üniversitesi Bilgisayar Mühendisliği (n=64), Bilgisayar Programcılığı (n=23), Web Tasarımı ve Kodlama (n=12) ve Kırklareli Üniversitesi Yazılım Mühendisliği (n=142) tüm sınıflar düzeyinde toplam 241 öğrenciden oluşmaktadır. Araştırma, nicel yaklaşımın kullanıldığı korelasyonel, kesitsel ve deneysel olmayan karma araştırma yöntemiyle anket uygulanarak yürütülmüştür. Anket soruları araştırmacılar tarafından hazırlanmıştır. Anket sonuçları literatürdeki benzer çalışmalarla karşılaştırılarak yazılım geliştirme/programla eğitimi ve iş gücüne yönelik bazı çıkarımlar tartışılmıştır. Katılımcıların görüşlerine göre, yazılım mühendisliği ve geliştirme alanında yapay zekâ uygulamalarının artan kullanımının gelecekteki profesyonel ihtiyaçları, iş güvenliğini ve kişisel gelişim gereksinimlerini önemli ölçüde etkileyeceği öne çıkmıştır. Ayrıca, yazılım ve uygulama geliştirme alanlarındaki yeteneklerin yanı sıra siber güvenliğin de önemli bir ilgi odağı olduğu tespit edilmiştir. Bu bağlamda yazılım geliştirme ve ilgili alanlarda öğrencilerin yeteneklerini ve yeterliliklerini artırmaya yönelik çeşitli öneriler sunulmuştur.

Etik Beyan

Bu araştırma, Trakya Üniversitesi Etik Kulunun 24.04.2024 tarih ve 2024.04.26 sayılı toplantısında alınan kararla etik kurallara uygun bir şekilde yürütülmüştür. Ayrıca Kırklareli Üniversitesi Mühendislik Fakültesi dekanlığının 29.03.2024 tarih ve E-12203248-199-119159 sayılı evrak ile araştırmanın uygulanması uygun görülmüştür. Ankete anonim olarak çevrimiçi erişmeden önce veya odak gruplarına başlamadan önce çalışmaya katılan tüm denekler bilgilendirilmiştir.

Kaynakça

  • Brosseau-Liard, P. E., & Savalei, V. (2014). Adjusting incremental fit indices for nonnormality. Multivariate Behavioral Research, 49(5), 460-470. https://doi.org/10.1080/00273171.2014.933697
  • Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3), 233-247. https://doi.org/10.1016/0165-0114(85)90090-9
  • Bull, C., & Kharrufa, A. (2023). Generative AI assistants in software development education: A vision for integrating generative AI into educational practice, not instinctively defending against it. arXiv. https://doi.org/10.48550/arXiv.2303.13936
  • Cao, X. (2023). The application of structural equation model in psychological research. CNS Spectrums, 28(S1), S17-S19. https://doi.org/10.1017/S1092852923000858
  • Chang, D.-Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649-655. https://doi.org/10.1016/0377-2217(95)00300-2
  • Cortina, J. M., Green, J. P., Keeler, K. R., & Vandenberg, R. J. (2017). Degrees of freedom in SEM: Are we testing the models that we claim to test? Organizational Research Methods, 20(3), 350-378. https://doi.org/10.1177/1094428116676345
  • Creswell, J. W. (2019). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (6th ed.). Pearson.
  • Dalal, S., Agrawal, A., Dahiya, N., & Verma, J. (2020). Software process improvement assessment for cloud application based on fuzzy analytical hierarchy process method. In O. Gervasi, B. Murgante, S. Misra, C. Garau, I. Blečić, D. Taniar, B. O. Apduhan, A. M. A. C. Rocha, E. Tarantino, C. M. Torre, & Y. Karaca (Eds.), Computational science and its applications – ICCSA 2020 (Vol. 12252, pp. 989-1001). Springer International Publishing. https://doi.org/10.1007/978-3-030-58811-3_70
  • Eurobarometer. (2017). Eurobarometer. https://europa.eu/eurobarometer/surveys/detail/2160
  • Hidalgo Suarez, C. G., Bucheli-Guerrero, V. A., & Ordóñez-Eraso, H. A. (2023). Artificial intelligence and computer-supported collaborative learning in programming: A systematic mapping study. Tecnura, 27(75), 175-206. https://doi.org/10.14483/22487638.19637
  • Ho, W. (2008). Integrated analytic hierarchy process and its applications – A literature review. European Journal of Operational Research, 186(1), 211-228. https://doi.org/10.1016/j.ejor.2007.01.004
  • Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
  • Jasra, B., & Dubey, S. K. (2019). Reliability assessment of component-based software system using fuzzy-AHP. In M. N. Hoda, N. Chauhan, S. M. K. Quadri, & P. R. Srivastava (Eds.), Software engineering (Vol. 731, pp. 663-670). Springer Singapore. https://doi.org/10.1007/978-981-10-8848-3_64
  • Jionghao, L., Eason, C., Gurung, A., & Koedinger, K. (2024). MuFIN: A framework for automating multimodal feedback generation using generative artificial intelligence. OSF Preprints. https://doi.org/10.35542/osf.io/3asxz
  • Khan, M. A., Parveen, A., & Sadiq, M. (2014). A method for the selection of software development life cycle models using analytic hierarchy process. 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), 534-540. https://doi.org/10.1109/ICICICT.2014.6781338
  • Kline, R. B. (2023). Structural equation modeling. In A. L. Nichols & J. Edlund (Eds.), The Cambridge handbook of research methods and statistics for the social and behavioral sciences (1st ed., pp. 535-558). Cambridge University Press. https://doi.org/10.1017/9781009010054.026
  • Liubchenko, V. (2022). Specific aspects of software development process for AI/ML-based systems. 2022 IEEE 17th International Conference on Computer Sciences and Information Technologies (CSIT), 470-473. https://doi.org/10.1109/CSIT56902.2022.10000821
  • Mai, R., Niemand, T., & Kraus, S. (2021). A tailored-fit model evaluation strategy for better decisions about structural equation models. Technological Forecasting and Social Change, 173, 121142. https://doi.org/10.1016/j.techfore.2021.121142
  • Marutschke, D. M., White, J., & Serdult, U. (2021). Student perception of software engineering factors supported by machine learning. 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 1-6. https://doi.org/10.1109/ICECCME52200.2021.9591101
  • McMillan, J. H., & Schumacher, S. (2010). Research in education: Evidence-based inquiry (7th ed.). Pearson.
  • Miksza, P., Shaw, J. T., Kapalka Richerme, L., Hash, P. M., Hodges, D. A., & Cassidy Parker, E. (2023). Quantitative descriptive and correlational research. In P. Miksza, J. T. Shaw, L. Kapalka Richerme, P. M. Hash, & D. A. Hodges (Eds.), Music education research (1st ed., pp. 241-C12P143). Oxford University Press. https://doi.org/10.1093/oso/9780197639757.003.0012
  • Monteiro, S., Almeida, L. S., & García-Aracil, A. (2015). Students’ perceptions of competencies by the end of a masters’ degree. Revista de Estudios e Investigación en Psicología y Educación, 2(1), 41-46. https://doi.org/10.17979/reipe.2015.2.1.932
  • Morales-Chan, M., Amado-Salvatierra, H. R., & Hernandez-Rizzardini, R. (2024). AI-driven content creation: Revolutionizing educational materials. Proceedings of the Eleventh ACM Conference on Learning @ Scale, 556-558. https://doi.org/10.1145/3657604.3664640
  • Mulaik, S. A., James, L. R., Van Alstine, J., Bennett, N., Lind, S., & Stilwell, C. D. (1989). Evaluation of goodness-of-fit indices for structural equation models. Psychological Bulletin, 105(3), 430-445. https://doi.org/10.1037/0033-2909.105.3.430
  • Ozkaya, I. (2023). The next frontier in software development: AI-augmented software development processes. IEEE Software, 40(4), 4-9. https://doi.org/10.1109/MS.2023.3278056
  • Pagadala, S. P., V., S., P., V., & Jha, G. K. (2023). An overview of structural equation modeling and its application in social sciences research. In C. A. Saliya (Ed.), Advances in knowledge acquisition, transfer, and management (pp. 145-163). IGI Global. https://doi.org/10.4018/978-1-6684-6859-3.ch010
  • Perdana, P. N., Armeliza, D., Khairunnisa, H., & Nasution, H. (2023). Research data processing through structural equation model-partial least square (SEM-PLS) method. Jurnal Pemberdayaan Masyarakat Madani (JPMM), 7(1), 44-50. https://doi.org/10.21009/JPMM.007.1.05
  • Saaty, R. W. (1987). The analytic hierarchy process—What it is and how it is used. Mathematical Modelling, 9(3-5), 161-176. https://doi.org/10.1016/0270-0255(87)90473-8
  • Savalei, V. (2021). Improving fit indices in structural equation modeling with categorical data. Multivariate Behavioral Research, 56(3), 390-407. https://doi.org/10.1080/00273171.2020.1717922
  • Setia, M. S. (2023). Cross-sectional studies. In A. L. Nichols & J. Edlund (Eds.), The Cambridge handbook of research methods and statistics for the social and behavioral sciences (1st ed., pp. 269-291). Cambridge University Press. https://doi.org/10.1017/9781009010054.014
  • Taha, H. A., & Nawaiseh, M. B. (2023). A response to “Patient’s perceptions and attitudes towards medical student’s involvement in their healthcare at a teaching hospital in Jordan: A cross sectional study” [Response to letter]. Patient Preference and Adherence, 17, 1159-1160. https://doi.org/10.2147/PPA.S416850
  • Thakkar, J. J. (2021). Analytic hierarchy process (AHP). In J. J. Thakkar, Multi-criteria decision making (Vol. 336, pp. 33-62). Springer Singapore. https://doi.org/10.1007/978-981-33-4745-8_3
  • Vaidya, O. S., & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of Operational Research, 169(1), 1-29. https://doi.org/10.1016/j.ejor.2004.04.028
  • Pardosi, A. B. V., Xu, S., Umurohmi, U., Nurdiana, N., & Sabur, F. (2024). Implementation of an artificial intelligence-based learning management system for adaptive learning. Al Fikrah: Jurnal Manajemen Pendidikan. https://doi.org/10.31958/jaf.v12i1.12548
  • Watson, C., & Li, F. W. B. (2014). Failure rates in introductory programming revisited. Proceedings of the 2014 Conference on Innovation & Technology in Computer Science Education (ITiCSE ’14), 39-44. https://doi.org/10.1145/2591708.2591749
  • Yaghoobi, T. (2018). Prioritizing key success factors of software projects using fuzzy AHP. Journal of Software: Evolution and Process, 30(1), e1891. https://doi.org/10.1002/smr.1891
  • Yasin, M. I. (2022). Youth perceptions and attitudes about artificial intelligence. Izvestiya of Saratov University. Philosophy. Psychology. Pedagogy, 22(2), 197-201. https://doi.org/10.18500/1819-7671-2022-22-2-197-201
  • Yilmaz, R., & Karaoglan Yilmaz, F. G. (2023). The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 4, 100147. https://doi.org/10.1016/j.caeai.2023.100147
  • Yonatha, A., Albuquerque, D., Dantas Filho, E., Muniz, F., Farias Santos, K. de, Perkusich, M., Almeida, H., & Perkusich, A. (2024). AICodeReview: Advancing code quality with AI-enhanced reviews. SoftwareX. https://doi.org/10.1016/j.softx.2024.101677
  • Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3312874

Integration of Skill Depth and Language Proficiency into Artificial Intelligence in Software Development Learning

Yıl 2024, , 382 - 399, 20.12.2024
https://doi.org/10.32329/uad.1519587

Öz

Generative artificial intelligence models are very effective in many fields today. Recently, it has been frequently discussed that it has a great impact on software development and programming. In this study, a questionnaire was applied to individuals studying software, computer and programming at undergraduate and associate degree level in order to learn their thoughts about this rapidly developing and rapidly strengthening technology. The study group of the research consists of a total of 241 students from Trakya University Computer Engineering (n=64), Computer Programming (n=23), Web Design and Coding (n=12) and Kırklareli University Software Engineering (n=142) in the spring semester of the 2023-2024 academic year. The research was conducted by applying a questionnaire with a correlational, cross-sectional and non-experimental mixed research method using a quantitative approach. The survey questions were prepared by the researchers. The survey results were compared with similar studies in the literature and some implications for software development/programming education and workforce were discussed. According to the views of the participants, the increasing use of artificial intelligence applications in software engineering and development will significantly affect future professional needs, job security and personal development requirements. In addition to skills in software and application development, cybersecurity was also identified as an important focus of interest. In this context, various suggestions are presented to increase students’ skills and competencies in software development and related fields.

Kaynakça

  • Brosseau-Liard, P. E., & Savalei, V. (2014). Adjusting incremental fit indices for nonnormality. Multivariate Behavioral Research, 49(5), 460-470. https://doi.org/10.1080/00273171.2014.933697
  • Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3), 233-247. https://doi.org/10.1016/0165-0114(85)90090-9
  • Bull, C., & Kharrufa, A. (2023). Generative AI assistants in software development education: A vision for integrating generative AI into educational practice, not instinctively defending against it. arXiv. https://doi.org/10.48550/arXiv.2303.13936
  • Cao, X. (2023). The application of structural equation model in psychological research. CNS Spectrums, 28(S1), S17-S19. https://doi.org/10.1017/S1092852923000858
  • Chang, D.-Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649-655. https://doi.org/10.1016/0377-2217(95)00300-2
  • Cortina, J. M., Green, J. P., Keeler, K. R., & Vandenberg, R. J. (2017). Degrees of freedom in SEM: Are we testing the models that we claim to test? Organizational Research Methods, 20(3), 350-378. https://doi.org/10.1177/1094428116676345
  • Creswell, J. W. (2019). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (6th ed.). Pearson.
  • Dalal, S., Agrawal, A., Dahiya, N., & Verma, J. (2020). Software process improvement assessment for cloud application based on fuzzy analytical hierarchy process method. In O. Gervasi, B. Murgante, S. Misra, C. Garau, I. Blečić, D. Taniar, B. O. Apduhan, A. M. A. C. Rocha, E. Tarantino, C. M. Torre, & Y. Karaca (Eds.), Computational science and its applications – ICCSA 2020 (Vol. 12252, pp. 989-1001). Springer International Publishing. https://doi.org/10.1007/978-3-030-58811-3_70
  • Eurobarometer. (2017). Eurobarometer. https://europa.eu/eurobarometer/surveys/detail/2160
  • Hidalgo Suarez, C. G., Bucheli-Guerrero, V. A., & Ordóñez-Eraso, H. A. (2023). Artificial intelligence and computer-supported collaborative learning in programming: A systematic mapping study. Tecnura, 27(75), 175-206. https://doi.org/10.14483/22487638.19637
  • Ho, W. (2008). Integrated analytic hierarchy process and its applications – A literature review. European Journal of Operational Research, 186(1), 211-228. https://doi.org/10.1016/j.ejor.2007.01.004
  • Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
  • Jasra, B., & Dubey, S. K. (2019). Reliability assessment of component-based software system using fuzzy-AHP. In M. N. Hoda, N. Chauhan, S. M. K. Quadri, & P. R. Srivastava (Eds.), Software engineering (Vol. 731, pp. 663-670). Springer Singapore. https://doi.org/10.1007/978-981-10-8848-3_64
  • Jionghao, L., Eason, C., Gurung, A., & Koedinger, K. (2024). MuFIN: A framework for automating multimodal feedback generation using generative artificial intelligence. OSF Preprints. https://doi.org/10.35542/osf.io/3asxz
  • Khan, M. A., Parveen, A., & Sadiq, M. (2014). A method for the selection of software development life cycle models using analytic hierarchy process. 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), 534-540. https://doi.org/10.1109/ICICICT.2014.6781338
  • Kline, R. B. (2023). Structural equation modeling. In A. L. Nichols & J. Edlund (Eds.), The Cambridge handbook of research methods and statistics for the social and behavioral sciences (1st ed., pp. 535-558). Cambridge University Press. https://doi.org/10.1017/9781009010054.026
  • Liubchenko, V. (2022). Specific aspects of software development process for AI/ML-based systems. 2022 IEEE 17th International Conference on Computer Sciences and Information Technologies (CSIT), 470-473. https://doi.org/10.1109/CSIT56902.2022.10000821
  • Mai, R., Niemand, T., & Kraus, S. (2021). A tailored-fit model evaluation strategy for better decisions about structural equation models. Technological Forecasting and Social Change, 173, 121142. https://doi.org/10.1016/j.techfore.2021.121142
  • Marutschke, D. M., White, J., & Serdult, U. (2021). Student perception of software engineering factors supported by machine learning. 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 1-6. https://doi.org/10.1109/ICECCME52200.2021.9591101
  • McMillan, J. H., & Schumacher, S. (2010). Research in education: Evidence-based inquiry (7th ed.). Pearson.
  • Miksza, P., Shaw, J. T., Kapalka Richerme, L., Hash, P. M., Hodges, D. A., & Cassidy Parker, E. (2023). Quantitative descriptive and correlational research. In P. Miksza, J. T. Shaw, L. Kapalka Richerme, P. M. Hash, & D. A. Hodges (Eds.), Music education research (1st ed., pp. 241-C12P143). Oxford University Press. https://doi.org/10.1093/oso/9780197639757.003.0012
  • Monteiro, S., Almeida, L. S., & García-Aracil, A. (2015). Students’ perceptions of competencies by the end of a masters’ degree. Revista de Estudios e Investigación en Psicología y Educación, 2(1), 41-46. https://doi.org/10.17979/reipe.2015.2.1.932
  • Morales-Chan, M., Amado-Salvatierra, H. R., & Hernandez-Rizzardini, R. (2024). AI-driven content creation: Revolutionizing educational materials. Proceedings of the Eleventh ACM Conference on Learning @ Scale, 556-558. https://doi.org/10.1145/3657604.3664640
  • Mulaik, S. A., James, L. R., Van Alstine, J., Bennett, N., Lind, S., & Stilwell, C. D. (1989). Evaluation of goodness-of-fit indices for structural equation models. Psychological Bulletin, 105(3), 430-445. https://doi.org/10.1037/0033-2909.105.3.430
  • Ozkaya, I. (2023). The next frontier in software development: AI-augmented software development processes. IEEE Software, 40(4), 4-9. https://doi.org/10.1109/MS.2023.3278056
  • Pagadala, S. P., V., S., P., V., & Jha, G. K. (2023). An overview of structural equation modeling and its application in social sciences research. In C. A. Saliya (Ed.), Advances in knowledge acquisition, transfer, and management (pp. 145-163). IGI Global. https://doi.org/10.4018/978-1-6684-6859-3.ch010
  • Perdana, P. N., Armeliza, D., Khairunnisa, H., & Nasution, H. (2023). Research data processing through structural equation model-partial least square (SEM-PLS) method. Jurnal Pemberdayaan Masyarakat Madani (JPMM), 7(1), 44-50. https://doi.org/10.21009/JPMM.007.1.05
  • Saaty, R. W. (1987). The analytic hierarchy process—What it is and how it is used. Mathematical Modelling, 9(3-5), 161-176. https://doi.org/10.1016/0270-0255(87)90473-8
  • Savalei, V. (2021). Improving fit indices in structural equation modeling with categorical data. Multivariate Behavioral Research, 56(3), 390-407. https://doi.org/10.1080/00273171.2020.1717922
  • Setia, M. S. (2023). Cross-sectional studies. In A. L. Nichols & J. Edlund (Eds.), The Cambridge handbook of research methods and statistics for the social and behavioral sciences (1st ed., pp. 269-291). Cambridge University Press. https://doi.org/10.1017/9781009010054.014
  • Taha, H. A., & Nawaiseh, M. B. (2023). A response to “Patient’s perceptions and attitudes towards medical student’s involvement in their healthcare at a teaching hospital in Jordan: A cross sectional study” [Response to letter]. Patient Preference and Adherence, 17, 1159-1160. https://doi.org/10.2147/PPA.S416850
  • Thakkar, J. J. (2021). Analytic hierarchy process (AHP). In J. J. Thakkar, Multi-criteria decision making (Vol. 336, pp. 33-62). Springer Singapore. https://doi.org/10.1007/978-981-33-4745-8_3
  • Vaidya, O. S., & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of Operational Research, 169(1), 1-29. https://doi.org/10.1016/j.ejor.2004.04.028
  • Pardosi, A. B. V., Xu, S., Umurohmi, U., Nurdiana, N., & Sabur, F. (2024). Implementation of an artificial intelligence-based learning management system for adaptive learning. Al Fikrah: Jurnal Manajemen Pendidikan. https://doi.org/10.31958/jaf.v12i1.12548
  • Watson, C., & Li, F. W. B. (2014). Failure rates in introductory programming revisited. Proceedings of the 2014 Conference on Innovation & Technology in Computer Science Education (ITiCSE ’14), 39-44. https://doi.org/10.1145/2591708.2591749
  • Yaghoobi, T. (2018). Prioritizing key success factors of software projects using fuzzy AHP. Journal of Software: Evolution and Process, 30(1), e1891. https://doi.org/10.1002/smr.1891
  • Yasin, M. I. (2022). Youth perceptions and attitudes about artificial intelligence. Izvestiya of Saratov University. Philosophy. Psychology. Pedagogy, 22(2), 197-201. https://doi.org/10.18500/1819-7671-2022-22-2-197-201
  • Yilmaz, R., & Karaoglan Yilmaz, F. G. (2023). The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 4, 100147. https://doi.org/10.1016/j.caeai.2023.100147
  • Yonatha, A., Albuquerque, D., Dantas Filho, E., Muniz, F., Farias Santos, K. de, Perkusich, M., Almeida, H., & Perkusich, A. (2024). AICodeReview: Advancing code quality with AI-enhanced reviews. SoftwareX. https://doi.org/10.1016/j.softx.2024.101677
  • Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3312874
Toplam 40 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Yükseköğretim Politikaları, Yükseköğretim Çalışmaları (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Selçuk Yazar 0000-0001-6567-4995

Tülay Demiralay 0000-0001-8284-6699

Tolga Demirhan 0000-0001-9840-4457

Erken Görünüm Tarihi 20 Aralık 2024
Yayımlanma Tarihi 20 Aralık 2024
Gönderilme Tarihi 20 Temmuz 2024
Kabul Tarihi 25 Eylül 2024
Yayımlandığı Sayı Yıl 2024

Kaynak Göster

APA Yazar, S., Demiralay, T., & Demirhan, T. (2024). Yazılım Geliştirme Öğreniminde Beceri Derinliği ve Dil Yeterliliğinin Yapay Zekâ ile Entegrasyonu. Journal of University Research, 7(4), 382-399. https://doi.org/10.32329/uad.1519587

Articles published in the Journal of University Research (Üniversite Araştırmaları Dergisi - ÜAD) are licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License 32353.